# Copyright 2018-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file is distributed
# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language governing
# permissions and limitations under the License.
from __future__ import absolute_import

import os

import pytest
from sagemaker import utils

from .timeout import timeout
from ..local.ag_tools import AutoGluon
from ..... import invoke_sm_helper_function
from ...integration import RESOURCE_PATH, DEFAULT_TIMEOUT


@pytest.mark.model("autogluon")
@pytest.mark.integration("smexperiments")
@pytest.mark.skip_test_in_region
@pytest.mark.team("autogluon")
def test_training(ecr_image, sagemaker_regions, instance_type, framework_version):
    invoke_sm_helper_function(
        ecr_image, sagemaker_regions, _test_training_function, instance_type, framework_version
    )


def _test_training_function(ecr_image, sagemaker_session, instance_type, framework_version):
    ag = AutoGluon(
        entry_point=os.path.join(RESOURCE_PATH, "scripts", "train_tab.py"),
        role="SageMakerRole",
        instance_count=1,
        instance_type=instance_type,
        sagemaker_session=sagemaker_session,
        image_uri=ecr_image,
        framework_version=framework_version,
    )

    ag = _disable_sm_profiler(sagemaker_session.boto_region_name, ag)

    with timeout(minutes=DEFAULT_TIMEOUT):
        device = "cpu"
        data_path = os.path.join(RESOURCE_PATH, "data")
        s3_prefix = "autogluon_sm/{}".format(utils.sagemaker_timestamp())
        train_input = ag.sagemaker_session.upload_data(
            path=os.path.join(data_path, "training", f"train.{device}.csv"), key_prefix=s3_prefix
        )
        eval_input = ag.sagemaker_session.upload_data(
            path=os.path.join(data_path, "evaluation", f"eval.{device}.csv"), key_prefix=s3_prefix
        )
        config_input = ag.sagemaker_session.upload_data(
            path=os.path.join(data_path, "config", f"config.{device}.yaml"), key_prefix=s3_prefix
        )

        job_name = utils.unique_name_from_base("test-autogluon-image")
        ag.fit(
            {"config": config_input, "train": train_input, "test": eval_input}, job_name=job_name
        )


def _disable_sm_profiler(region, estimator):
    """Disable SMProfiler feature for China regions"""

    if region in ("cn-north-1", "cn-northwest-1"):
        estimator.disable_profiler = True
    return estimator
